Software, data and AI engineering

Intelligent systemsSoftware, data and models for real-world challenges.

We design and build software products, data platforms, AI systems and optimisation models for organisations that need technology they can rely on every day.

What we do
0+
Years of experience
0+
Sectors
3
Cloud platforms

Relevant professional experience

Nokia
BCC
What we do

Complex engineering.Designed for day-to-day use.

We take on work where code, data, models and infrastructure need to operate together. We begin by defining the outcome, the users and the constraints, then design around them.

Data engineering

Reliable, well-governed data platforms

We combine data sources, business rules and operating workflows in an architecture that makes quality, lineage, ownership and cost visible from the start.

Lakehouse architectures · regulated environments · global supply chains

AI and machine learning

Models that support real decisions

We develop predictive and generative AI around the data, permissions and people involved. Evaluation, deployment, monitoring, cost and ownership are part of the work, not an afterthought.

LLM applications · predictive models · document retrieval

Mathematical optimisation

Better decisions under complex constraints

We model planning, allocation, routing and scheduling around the constraints that apply in practice. Optimisation and simulation then support decisions people can explain and act on.

Routing · resource allocation · planning under multiple constraints

Scientific computing

Reproducible scientific software

We turn experimental workflows into maintainable software without losing scientific rigour. Data, parameters and results remain traceable, repeatable and ready for further analysis.

Genomics platforms · oncology data · published biomedical research

Custom software

Internal tools that improve operations

We build portals, applications and services around the way each organisation works. Product, backend and data are designed together to simplify operations and reduce manual work.

Operational portals · public-sector processes · backend and data services

Experience

More than 20 years solving demanding problems

Selected work across enterprise platforms, regulated banking, consumer goods, global digital services, genomics and oncology. In every case, the technology had to withstand daily use and deliver a practical result.

Selected experience

01/05

Global consumer goods

Cloud governance and architecture for a global organisation

Cloud architecture

Led the technical design of an Azure platform for a global consumer goods group, bringing internal teams, vendors and delivery partners together around a shared architecture, operating model and Terraform-managed infrastructure.

Azure governanceTerraform infrastructureVendor alignmentOperating standards

01/05

BCC · IBM / Viewnext

A large-scale data platform for banking operations

Data engineering

Helped design and build a data lake and real-time Kafka pipelines for BCC, together with an operational view of system state and data flows for technical and business teams.

Data lake architectureReal-time KafkaCross-team adoptionTechnical training

02/05

Nokia · Microsoft MixRadio

Recommendation systems for a global music service

Recommendation systems

Built the AWS data layer behind MixRadio's recommendation service, turning data science algorithms into personalisation for millions of users. The work also covered APIs, microservices and catalogue tools for editorial teams.

Recommendation pipelinesAWS in productionMillions of usersInternal tools

03/05

Coral Genomics

Petabyte-scale genomics for machine learning

AI for genomics

Designed and released DNARecords, a sparse genomics format and open-source SDK that transforms large VCF/BGEN datasets into efficient representations for machine learning and deep learning.

bioRxiv publicationOpen-source SDKVCF/BGEN conversionGenomics infrastructure

Oncko

Software and AI for oncology research

AI for oncology

Built scientific software and data systems for drug-combination research: large-scale matrix clustering, bioinformatics pipeline orchestration, LLM-assisted extraction, data harmonisation and hypothesis tracking.

Drug combinationsBioinformatics pipelinesLLM extractionHypothesis management

05/05

Global consumer goods

Cloud governance and architecture for a global organisation

Cloud architecture

Led the technical design of an Azure platform for a global consumer goods group, bringing internal teams, vendors and delivery partners together around a shared architecture, operating model and Terraform-managed infrastructure.

Azure governanceTerraform infrastructureVendor alignmentOperating standards

01/05

BCC · IBM / Viewnext

A large-scale data platform for banking operations

Data engineering

Helped design and build a data lake and real-time Kafka pipelines for BCC, together with an operational view of system state and data flows for technical and business teams.

Data lake architectureReal-time KafkaCross-team adoptionTechnical training

02/05

Nokia · Microsoft MixRadio

Recommendation systems for a global music service

Recommendation systems

Built the AWS data layer behind MixRadio's recommendation service, turning data science algorithms into personalisation for millions of users. The work also covered APIs, microservices and catalogue tools for editorial teams.

Recommendation pipelinesAWS in productionMillions of usersInternal tools

03/05

Coral Genomics

Petabyte-scale genomics for machine learning

AI for genomics

Designed and released DNARecords, a sparse genomics format and open-source SDK that transforms large VCF/BGEN datasets into efficient representations for machine learning and deep learning.

bioRxiv publicationOpen-source SDKVCF/BGEN conversionGenomics infrastructure

Oncko

Software and AI for oncology research

AI for oncology

Built scientific software and data systems for drug-combination research: large-scale matrix clustering, bioinformatics pipeline orchestration, LLM-assisted extraction, data harmonisation and hypothesis tracking.

Drug combinationsBioinformatics pipelinesLLM extractionHypothesis management

05/05

Global consumer goods

Cloud governance and architecture for a global organisation

Cloud architecture

Led the technical design of an Azure platform for a global consumer goods group, bringing internal teams, vendors and delivery partners together around a shared architecture, operating model and Terraform-managed infrastructure.

Azure governanceTerraform infrastructureVendor alignmentOperating standards

01/05

BCC · IBM / Viewnext

A large-scale data platform for banking operations

Data engineering

Helped design and build a data lake and real-time Kafka pipelines for BCC, together with an operational view of system state and data flows for technical and business teams.

Data lake architectureReal-time KafkaCross-team adoptionTechnical training

02/05

Nokia · Microsoft MixRadio

Recommendation systems for a global music service

Recommendation systems

Built the AWS data layer behind MixRadio's recommendation service, turning data science algorithms into personalisation for millions of users. The work also covered APIs, microservices and catalogue tools for editorial teams.

Recommendation pipelinesAWS in productionMillions of usersInternal tools

03/05

Coral Genomics

Petabyte-scale genomics for machine learning

AI for genomics

Designed and released DNARecords, a sparse genomics format and open-source SDK that transforms large VCF/BGEN datasets into efficient representations for machine learning and deep learning.

bioRxiv publicationOpen-source SDKVCF/BGEN conversionGenomics infrastructure

Oncko

Software and AI for oncology research

AI for oncology

Built scientific software and data systems for drug-combination research: large-scale matrix clustering, bioinformatics pipeline orchestration, LLM-assisted extraction, data harmonisation and hypothesis tracking.

Drug combinationsBioinformatics pipelinesLLM extractionHypothesis management

05/05

How we work

From the first conversation
to the day after handover

Each project starts by agreeing a concrete outcome and understanding who needs it, what constraints apply and how we will tell whether it works. From there, the work moves forward in increments that can be reviewed and used.

Not left until the end

Data quality, permissions, failure modes, operating cost and documentation are considered while the system is being built.

01

Agree the outcome

We review the starting point with the people who know the problem. Together we define what needs to change, who it is for and how we will tell whether it works.

02

Resolve the important unknowns early

Before building at scale, we check the data, integrations and decisions that could shape the whole project. If something is still unknown, we say so.

03

Build in useful increments

The work is divided into pieces that can be shown, tested and corrected. That lets us test decisions against real use without waiting until the end.

04

Prepare for the day after delivery

Before handover, we check deployment, access, alerts and what happens if something fails. The system has to be usable and maintainable day to day.

Sunny Data

Senior technical leadership
without the hand-offs

Sunny Data is an independent technology consultancy. The founder leads every project and stays involved in defining the problem, making the architectural decisions and building the solution.

The technical lead is the same person from the first conversation through to handover. There is no need to brief someone new as the work moves from one phase to the next.

Professional background

Mathematician · Technical director

More than 20 years designing, building and running technology across telecommunications, entertainment, consumer goods, finance and biomedical research.

IBM · Nokia · Microsoft · BCC

During the project

01

The important questions come first

The first conversation already covers the data, constraints, timescales and risks. The difficult parts are not left for later.

02

Options are explained

If there is more than one viable option, we set out what each one offers and what it demands. The team knows what was chosen and why.

03

Architecture does not stop at a diagram

We check decisions against the code, data and infrastructure. If something does not work as expected, we correct it early.

04

A handover the team can use

The team receives the repositories and documentation, and understands why the key decisions were made. It can maintain what has been built without depending on Sunny Data.

New projects

When the problem demands
judgement and delivery

Bring us the outcome, the constraints and what is getting in the way. We will help you find a practical route forward.

Sunny Data

Company

Independent consultancy for software products, data platforms, AI and mathematical optimisation.

Sunny Data Technologies S.L.

NIF B93937076

Carretera Sacramento, Edificio Sede Científica PITA, Campus UAL, s/n, Puerta 20, 04120 Almería, Spain

hello@sunnydata.es

© 2026 Sunny Data Technologies S.L. All rights reserved.

Engineering for demanding technical work.